Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis
arxiv(2024)
摘要
Thyroid cancer is an increasing global health concern that requires advanced
diagnostic methods. The application of AI and radiomics to thyroid cancer
diagnosis is examined in this review. A review of multiple databases was
conducted in compliance with PRISMA guidelines until October 2023. A
combination of keywords led to the discovery of an English academic publication
on thyroid cancer and related subjects. 267 papers were returned from the
original search after 109 duplicates were removed. Relevant studies were
selected according to predetermined criteria after 124 articles were eliminated
based on an examination of their abstract and title. After the comprehensive
analysis, an additional six studies were excluded. Among the 28 included
studies, radiomics analysis, which incorporates ultrasound (US) images,
demonstrated its effectiveness in diagnosing thyroid cancer. Various results
were noted, some of the studies presenting new strategies that outperformed the
status quo. The literature has emphasized various challenges faced by AI
models, including interpretability issues, dataset constraints, and operator
dependence. The synthesized findings of the 28 included studies mentioned the
need for standardization efforts and prospective multicenter studies to address
these concerns. Furthermore, approaches to overcome these obstacles were
identified, such as advances in explainable AI technology and personalized
medicine techniques. The review focuses on how AI and radiomics could transform
the diagnosis and treatment of thyroid cancer. Despite challenges, future
research on multidisciplinary cooperation, clinical applicability validation,
and algorithm improvement holds the potential to improve patient outcomes and
diagnostic precision in the treatment of thyroid cancer.
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